Combining Pattern Classifiers: Methods and Algorithms

  title={Combining Pattern Classifiers: Methods and Algorithms},
  author={Ludmila I. Kuncheva},
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Dynamic Classifier Systems and Their Applications to Random Forest Ensembles

This paper provides a general framework for dynamic classifier systems, which use dynamic confidence measures to adapt to a particular pattern, and experiments with random forests show that dynamic classifiers systems can significantly outperform both confidence-free and staticclassifier systems.

A New Emerging Pattern Mining Algorithm and Its Application in Supervised Classification

A novel algorithm to find emerging patterns without global discretization, which uses an accurate estimation of the support threshold, and attains higher accuracy than other understandable classifiers, while being competitive with Nearest Neighbors and Support Vector Machines classifiers.

A Learning Algorithm for the Optimum-Path Forest Classifier

A learning algorithm is introduced for the OPF-based classifiers and it is shown that a classifier can learns with its own errors without increasing its training set.

A Team Is Superior to an Individual

This chapter deals with connecting classifiers into a team. It explains the concepts of aggregation function and confidence, the difference between general teams and ensembles and main methods of

Use of Structured Pattern Representations for Combining Classifiers

Combination of classifiers has been applied using tree pattern representation in combination with strings and vectors for a handwritten character classification task in order to save computational cost.

Ensemble of Classifiers with Modification of Confidence Values

The algorithm which modifies the confidence values of the ensemble of classifiers is proposed and results show that the proposed method is a promising method for the development of multiple classifiers systems.

A Model-Based Approach for Building Optimum Classification Cascades

In this chapter, a model-based algorithm of automatic generation of optimum classification cascades is devised and given a large pool of classifiers, it builds a cascade that achieves the lowest possible recognition time while preserving the accuracy of the most powerful classifier in the pool.

Spam Detection System Combining Cellular Automata and Naïve Bayes Classifier

A novel method combining multiple classifiers diversified both by feature selection and different classifiers to determine whether it can more accurately detect Spam is evaluated.

Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography

  • L. Rokach
  • Computer Science
    Comput. Stat. Data Anal.
  • 2009



Clustering-and-selection model for classifier combination

  • L. Kuncheva
  • Computer Science
    KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516)
  • 2000
A simple clustering-and-selection algorithm based on a probabilistic interpretation of classifier selection, where the most successful classifier for a given cluster is nominated to label the inputs in the Voronoi cell of the cluster centroid.

Decision Combination in Multiple Classifier Systems

This work proposes three methods based on the highest rank, the Borda count, and logistic regression for class set reranking that have been tested in applications of degraded machine-printed characters and works from large lexicons, resulting in substantial improvement in overall correctness.

Data Dependence in Combining Classifiers

A new categorization of combining schemes based on their dependence on the data patterns being classified is presented, arguing that data dependent, and especially explicitly datadependent, approaches represent the highest potential for improved performance.

Neural and statistical classifiers-taxonomy and two case studies

This work gives a tutorial overview in which popular classifiers are grouped into distinct categories according to their underlying mathematical principles; also, what makes a classifier neural is assessed.

A multinomial selection procedure for evaluating pattern recognition algorithms

A note on comparing classifiers

  • R. Duin
  • Computer Science
    Pattern Recognit. Lett.
  • 1996

A Comparative Evaluation of Sequential Feature Selection Algorithms

Positive empirical results are reported on variants of sequential feature selection that might be more appropriate for some performance tasks, and it is argued for their serious consideration in similar learning tasks.

Adaptive pattern recognition and neural networks

This is a book that will show you even new to old thing, and when you are really dying of adaptive pattern recognition and neural networks, just pick this book; it will be right for you.

Introduction to Statistical Pattern Recognition

Two approaches to dimensionality reduction, namely feature selection (FS) and feature extraction (FE) are specified, though FS is a special case of FE, they are very different from a practical viewpoint and thus must be considered separately.

Methods of Combining Multiple Classifiers with Different Features and Their Applications to Text-Independent Speaker Identification

The simulations show that results yielded by the methods described in this paper are better than not only the individual classifiers' but also ones obtained by combining multiple classifiers with the same feature.